Within the field of computing, trending topic systems collect content items from content sources then generate, rank, sort, and display topics to users. These systems distill vast amounts of information and provide users with a summary of popular subjects or themes expressed in recently-posted content items. Several types of trending topic systems exist, some of which operate on social networks as the content sources. Twitter, for example, uses itself as the content source and Tweets generated by its users as the content items. It identifies trending phrases contained with these Tweets and lists the top-ranking to users. News aggregators are another type of trending topic system that operate on news websites as content sources and news articles as content items. Google News is one such example. It constantly monitors news websites for news articles and identifies commonalities between the news articles to generate, rank, and sort topics. As anyone skilled in the art will acknowledge, trending topic systems discussed herein generally operate on content items rich in words and phrases and authored by humans. Systems that operate on data largely numerical in nature, such as stock trading systems, fall outside of the scope of this art.
While useful, conventional trending topic systems only show users a static view of information and don't provide users with valuable information about how topics have changed since the user last checked a trending topic display. At any given point in time, a frequent user who checks the display every few minutes sees the same information as an infrequent user who checks the display once per day. The frequent user will likely see most of the same topics as the previous display and must consciously separate the new from the old. The infrequent user will likely see mostly new topics. However, the infrequent user likely missed other, possibly interesting topics that had trended at an earlier time and are no longer part of the current display. For both the frequent and infrequent users, conventional trending topic systems lack the capabilities to provide an ideal experience.
A system and method for displaying changes in trending topics to users requires unique topic comparison capabilities. When a user requests a display, a comparison is required between current topics and prior topics to determine which topics are new and which topics are old. Conventional trending topic systems are able to compare content item characteristics for the purpose of generating topics but fall short with regard to topic-to-topic comparison. Content items have a static structure. For the most part, content item characteristics don't change over time. Topics, on the other hand, are dynamic. The content items and characteristics associated with a topic change as the body of information across the network changes. Lacking the ability to adequately compare topics, conventional trending topic systems do not have the capacity to display changes in trending topics to users.